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676 results for “population density”
High-density genomic data reveal fine-scale population structure and pronounced islands of adaptive divergence in lake whitefish (Coregonus clupeaformis) from Lake Michigan
<p>Understanding patterns of genetic structure and adaptive variation in natural populations is crucial for informing conservation and management. Past genetic research using 11 microsatellite loci identified six genetic stocks of lake whitefish (<em>Coregonus clupeaformis</em>) within Lake Michigan, USA. However, ambiguity in genetic stock assignments suggested those neutral microsatellite markers did not provide adequate power for delineating lake whitefish stocks in this system, prompting calls for a genomics approach to investigate stock structure. Here, we generated a dense genomic dataset to characterize population structure and investigate patterns of neutral and adaptive genetic diversity among lake whitefish populations in Lake Michigan. Using Rapture sequencing, we genotyped 829 individuals collected from 17 baseline populations at 197,588 SNP markers after quality filtering. Although the overall pattern of genetic structure was similar to the previous microsatellite study, our genomic data provided several novel insights. Our results indicated a large genetic break between the northwestern and eastern sides of Lake Michigan, and we found a much greater level of population structure on the eastern side compared to the northwestern side. Collectively, we observed five genomic islands of adaptive divergence on five different chromosomes. Each island displayed a different pattern of population structure, suggesting that combinations of genotypes at these adaptive regions are facilitating local adaptation to spatially heterogenous selection pressures. Additionally, we identified a large linkage disequilibrium block of ~8.5 Mb on chromosome 20 that is suggestive of a putative inversion but with a low frequency of the minor haplotype. Our study provides a comprehensive assessment of population structure and adaptive variation that can help inform management of Lake Michigan's lake whitefish fishery and highlights the utility of incorporating adaptive loci into fisheries management. </p>
Population density does not affect seasonal regulation of reproductive physiology in male water voles
<p>Most small rodent species display cyclic fluctuations in their population density. The mechanisms behind these cyclical variations are not yet clearly understood. Density-dependent effects on reproductive function could affect these population variations. The fossorial water vole ecotype, Arvicola terrestris, undergoes a multi-year cycle dynamic with outbreak peaks. Here, we monitored different water vole populations over three years, in spring and autumn, to evaluate whether population density be related to male reproductive physiology. Our results show an effect of season and inter-annual factors in testes mass, plasmatic testosterone level, and androgen-dependent seminal vesicles mass. By contrast, population density does not affect any of these parameters, thus suggesting a lack of modulation of population dynamics of population density.</p>
Data from: Density matters: How population dynamics of house mice (Mus musculus) inform the epidemiology of Leptospira
<p>Rodents are maintenance hosts of numerous pathogens, and both their density and the pathogen prevalence determine the risk they pose to other animals or humans. However, density is often overlooked. We investigated a capture-mark-recapture-sampling strategy to study introduced mice (<em>Mus musculus</em>) and <em>Leptospira</em> as a model and demonstrate the advantages of a combined approach. We estimated population density and <em>Leptospira</em> prevalence in mice in a replicated longitudinal survey conducted between 2016 and 2018. Capture-mark-recapture sessions were undertaken at two sites in Spring and Autumn and blood and kidney samples were collected at the end of each session. Mouse density and areas of activity were estimated using spatially explicit capture-recapture (SECR) models and both were compared between <em>Leptospira</em> positive and negative mice. <em>Leptospira </em>exposure and shedding status were estimated using Microscopic Agglutination Test, and a combination of culture and <em>lipL32</em> PCR on kidneys. <em>Leptospira </em>prevalence was higher in spring (83% to 86%) than in autumn (31% to 37%) and mouse densities simultaneously varied from 3.6 to 55.9/ha. However, despite these variations in prevalence and density, the density of infected animals remained relatively constant over time (3 to 8/ha). Shedding or being seropositive was also associated with the activity of mice. Shedding or seropositive mice had a larger activity area, and seropositive mice were trapped on average one day earlier than seronegative mice. </p> <p><em>Synthesis and applications</em>. Our results show how understanding the population dynamics of pathogen-carrying rodents is critical in epidemiology. The wider movement patterns and easier encounters of positive mice highlight the possibility of biases in classical prevalence surveys and have implications for disease transmission within and between species. Importantly, and quite counter-intuitively, <em>Leptospira</em> prevalence was negatively associated with mouse density, resulting in a constant density of shedders that contradicts the conventional view of higher exposure risk at high rodent density. More broadly, such hybrid sampling designs can improve animal and disease control policies and better inform modelling studies by providing more parameter estimates than classical prevalence surveys.</p>
Fig. 2 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 2. Sampling localities of C. odoratus.
Fig. 9 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 9. Results of each trait variation in C. odoratus at different altitudes.
Data from: Phenotypic selection on an ornamental trait is not modulated by breeding density in a pied flycatcher population
<p>Most studies of phenotypic selection in the wild have focused on morphological and life-history traits and looked at abiotic (climatic) variation as the main driver of selection. Consequently, our knowledge of the effects of biotic environmental variation on phenotypic selection on sexual traits is scarce. Population density can be considered a proxy for the intensity of intra- and inter-sexual competition and could therefore be a key factor influencing the covariation between individual fitness and the expression of sexual traits. Here, we used an individual-based data set from a population of pied flycatchers (<em>Ficedula hypoleuca</em>) monitored over 24 years to analyse the effect of breeding density on phenotypic selection on dorsal plumage colouration, a heritable and sexually selected ornament in males of this species. Using the number of recruits as a fitness proxy, our results show overall stabilizing selection on male dorsal colouration, with intermediate phenotypes being favoured over extremely dark and dull individuals. However, our results did not support the hypothesis that breeding density mediates phenotypic selection on this sexual trait. We discuss the possible role of other biotic factors influencing selection on ornamental plumage.</p>
Fig. 5 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 5 - Orientation of the red squirrel dreys per study area.
Fig. 3 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 3 - Study areas in Lombardy. Box: geographic position of Lombardy (black) in Italy.
Fig. 2 in Shell size and population density of Cerastoderma glaucum Poiret 1789 (Mollusca: Bivalvia) in "Pomorie Lake" (Black Sea coast, Bulgaria)
Fig. 2. Population density of Cerastoderma glaucum per 1 m2 of each size group during seasons.
Fig. 1 in Shell size and population density of Cerastoderma glaucum Poiret 1789 (Mollusca: Bivalvia) in "Pomorie Lake" (Black Sea coast, Bulgaria)
Fig. 1. Map of the studied area (the location of the sampling sites is indicated with numbers).
Supporting data and script for "Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density" (Tallavaara et al.)
<p>This submission contains data and R-script that enable to reproduce the data manipulations and analyses in the paper “Productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” by Miikka Tallavaara, Jussi T. Eronen, and Miska Luoto (PNAS 2018 115 (6) 1232-1237, doi/10.1073/pnas.1715638115). Please, cite the above paper, if you use the files included in this Zenodo record in your work.</p> <p>Included in the submission are R-script as a pdf-file (Tallavaara_Data_analyses.pdf), global net primary productivity data (Tallavaara_Dataset_1.tif), global biodiversity data (Tallavaara_Dataset_2.tif), and global pathogen stress data (Tallavaara_Dataset_3.tif) as GeoTIFF-files. In addition, submission contain global hunter-gatherer data (Tallavaara_Dataset_4.xls) as xls-file. If these datasets are saved in the working directory they can be read in to the R using the included R-script (Tallavaara_Data_analyses).</p>
Figure 2 in Seasonal composition and population density of zooplankton in Lake Karaboğaz from the Kızılırmak Delta (Samsun, Turkey)
Figure 2. Cluster diagram based on salinity and electrical conductivity recorded at each station.
Figure 3 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 3. Average change in H. halys population in Rize in the years 2019 and 2021.
Figure 2 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 2. Average change in H. halys population in Artvin in the years 2019, 2020, and 2021.
Figure 1 in Distribution and population density of Halyomorpha halys (Stål, 1855) (Hemiptera: Pentatomidae) in Black Sea Region of Türkiye
Figure 1. Infested area in Middle and Eastern Black Sea Region.
TABLE 1 in Nesting evidence, density and vocalisations in a resident population of Savannah Sparrow Passerculus sandwichensis wetmorei in Guatemala
<p>TABLE 1 Mean (± SD) and range of signal characteristics of songs of breeding Savannah Sparrows <i>Passerculus sandwichensis wetmorei</i> in PRM Todos Santos Cuchumatán, dpto. Huehuetenango, Guatemala, in June 2016, <i>n</i> = 37 songs of four males.</p><table><thead><tr><th><b>Song section (see Fig. 5)</b></th><th><b>Duration (seconds)</b></th><th><b>Peak frequency (kHz)</b></th></tr></thead><tbody><tr><th>Entire song (<i>n</i> = 37)</th><td>2.5 ± 0.3 (2.1–3.1)</td><td></td></tr><tr><th>Introduction:</th><td></td><td></td></tr><tr><th><i>Chip</i> note (<i>n</i> = 97)</th><td>0.06 ± 0.01 (0.04–0.12)</td><td>7.906 ± 202 (6.938 –8.250)</td></tr><tr><th>Middle section:</th><td></td><td></td></tr><tr><th>Descending whistle (DW1) (<i>n</i> = 37)</th><td>0.10 ± 0.01 (0.08–0.11)</td><td>6.927 ± 132 (6.750 –7.125)</td></tr><tr><th>Trill (n = 37)</th><td>0.05 ± 0.01 (0.03–0.06)</td><td>6.471 ± 1.268 (4.125 –7.500)</td></tr><tr><th>Double <i>ch</i> note (<i>n</i> = 37)</th><td>0.08 ± 0.004 (0.07–0.10)</td><td>5.063 ± 378 (3.188 –5.625)</td></tr><tr><th>Dominant section:</th><td></td><td></td></tr><tr><th>Buzz (<i>n</i> = 37)</th><td>0.60 ± 0.06 (0.5–0.8)</td><td>6.456 ± 646 (4.688 –6.938)</td></tr><tr><th>Terminal section:</th><td></td><td></td></tr><tr><th>Descending whistle (DW2) (<i>n</i> = 37)</th><td>0.08 ± 0.005 (0.07–0.09)</td><td>7.566 ± 222 (7.313 –8.063)</td></tr><tr><th>Trill-whistle (n = 37)</th><td>0.29 ± 0.07 (0.16 – 0.39)</td><td>4.074 ± 355 (3.375 –4.313)</td></tr></tbody></table>
Counting the chorus: A bioacoustic indicator of population density
<p>Passive acoustic monitoring has grown in utility for tracking wildlife populations, though challenges remain when using acoustic detections to monitor population size and density. Distance sampling is considered the 'gold standard' for estimating animal densities but has several important limitations. Here, we have compiled data and code from a case study that demonstrates a fast bioacoustic analysis method leveraging a simple metric call density. Using three years of synchronously collected bioacoustic and point-transect distance sampling data for eight forest bird species native to Hawai‘i, including four endangered species, we found strong correlations between call density and distance sampling-based animal density estimates. These findings indicate that call density is a reliable indicator of animal density that can be used independently or combined with traditional monitoring methods. This approach could enhance passive acoustic monitoring by providing more sensitive population health indicators than commonly used detection/nondetection methods, facilitating prompt conservation and management decisions.</p>
The effect of tropical forest modification on primate population density and diversity
<b>Description: </b><p>This data represents orang-utan nest survey data, collected to investigate the effects of habitat disturbance on orang-utan populations. Our surveys were conducted in and around the SAFE project site, including areas of Ulu Segama forest reserves and surrounding oil palm estates, covering a total study area of ca. 13,000ha. We conducted nest surveys between April and August 2017, using the standing crop method. Transects were surveyed once by teams of two to four people walking at a steady pace of roughly 0.5km/hr. The data primarily contains perpendicular distances from directly under each nest we encountered to the transect line. We assigned a decay category to each nest, ranging from 1 (new nest) to 5 (heavily degraded). Additionally we recoded the location of each nest, the height of the nest and DBH of the host tree. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/30"><b>The effect of tropical forest modification on primate population density and diversity.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC - Human Modified Tropical Forest (HMTF) (Standard grant, NE/K016407/1, <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li><li>Primate Society of Great Britain (Conservation grant, NA, <a href="http://www.psgb.org/conservation_grants.php">http://www.psgb.org/conservation_grants.php</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.4(104))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5109892">here</a></p><p><b>Files: </b>This consists of 1 file: Orangutan_Transect_Data.xlsx</p><p><b>Orangutan_Transect_Data.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Orang-utan nest survey data</b> (described in worksheet Distance_data)</p><p>Description: Data from orang-utan nest surveys, using the standing crop method to collect perpendicular distances of orang-utan nests from line transects.</p><p>Number of fields: 13</p><p>Number of data rows: 677</p><p>Fields: </p><ul><li><b>Region</b>: Area in which the survey took place (Field type: id)</li><li><b>Habitat_Type</b>: Habitat type of the transect location (Field type: id)</li><li><b>Transect</b>: Transect ID (Field type: location)</li><li><b>TranLenght</b>: Transect length (Field type: numeric)</li><li><b>Side</b>: Side of the transect nest was located (L = left, R= Right and OT= On transect) (Field type: categorical)</li><li><b>Dist</b>: Perpendicular distance from direct under nest to the transect line (Field type: numeric)</li><li><b>Slope</b>: Angle of slope from transect line (measured to compensate for measuring perpendicular distance of steel slopes) (Field type: numeric)</li><li><b>Class</b>: Age class of east nest (From 1 = new nest to 5 = very old nest) (Field type: numeric)</li><li><b>Nest_Height</b>: Height of each nest in host tree (Field type: numeric trait)</li><li><b>DBH</b>: DBH of host tree (Field type: numeric)</li><li><b>Lat_Y</b>: Nest location latitude (Field type: latitude)</li><li><b>Long_X</b>: Nest location longitude (Field type: longitude)</li><li><b>Elevation</b>: Height above sea leave at each nest location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2017-04-01 to 2017-08-28</p><p><b>Latitudinal extent: </b>4.5607 to 4.7828</p><p><b>Longitudinal extent: </b>117.4636 to 117.7008</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Primates <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br></div><p></p>
Tables and Appendix indicate population density and distribution of Cheer Pheasant (Catreus wallichii) in different localities of AJ&K
<p><strong>Table 5 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>in different localities of AJ&K.</p> <p><strong>Table 6 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>in different sub-localities of AJ&K.</p> <p><strong>Table 7 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>across the months in AJ&K.</p> <p><strong>Appendix-I </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichi</em>) in Jhelum Velley, AJ&K.</p> <p><strong>Appendix-II </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Machiara National Park, Muzaffarabad AJ&K.</p> <p><strong>Appendix-III </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Phalla Game Reserve, Haveli, AJ&K.</p> <p><strong>Appendix-IV </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Nar Sher Ali Khan, Bagh, Azad Jammu & Kashmir.</p> <p> </p> <p> </p>
FIGURE 2 in Egg Number Varies With Population Density; A Study Of Three Oribatid Mite Species In Orchard Habitats In Egypt
FIGURE 2:: Average population densities of the three studied species as a function of sampling time. Means are given with their standard errors. For each vegetation type, means were taken over three different sites (cf. Fig. 1) and three replicate samples within a site.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.